code: def _wmape(
      y_obs: np.ndarray, y_pred: np.ndarray, se_pred: np.ndarray
  ) -> float:
      """Weighted mean absolute predictive error"""
      eps = np.finfo(y_obs.dtype).eps
      return float(
          np.sum(np.abs(y_pred - y_obs))
          / np.sum(np.abs(y_obs)).clip(min=eps)
      )
  
file_path: Ax/ax/utils/stats/model_fit_stats.py
imports: from typing import Dict, Mapping, Optional, Protocol
  import numpy
  from scipy.stats import fisher_exact, norm, pearsonr, spearmanr
label_desc: Use image node_id 8 for calling a global function with example usage: _wmape(y_obs, y_pred, se_pred) and returns: float